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Opposite and dynamic regulation
of the interferon response in metastatic and non-metastatic breast cancer
Apsana Lamsal1,2, Sonja Benedikte Andersen1,2, Ida Johansson1, Marina Vietri3,4, Ansooya Avinash Bokil2,6, Natalie Jayne Kurganovs5, Felicia Rylander1, Geir Bjørkøy1,2, Kristine Pettersen1,2* and Miriam S. Giambelluca2,7*
Background To our current understanding, solid tumors depend on suppressed local immune reactions, often elic- ited by the interaction between tumor cells and tumor microenvironment (TME) components. Despite an improved understanding of anti-cancer immune responses in the TME, it is still unclear how immuno-suppressive TME are formed and how some cancer cells survive and metastasize.
Methods To identify the major adaptations that cancer cells undergo during tumor development and progression, we compared the transcriptome and proteome from metastatic 66cl4 and non-metastatic 67NR cell lines in culture versus their corresponding mouse mammary primary tumors. Using confocal microscopy, RT-qPCR, flow cytometry and western blotting, we studied the signaling pathway and the mechanisms involved. In addition, we used public gene expression data from human breast cancer biopsies to evaluate the correlation between gene expression and clinical outcomes in patients.
Results We found that type I interferon (IFN-I) response was a key differentially regulated pathway between meta- static and non-metastatic cell lines and tumors. The IFN-I response was active in metastatic cancer cells in culture and markedly dampened when these cells formed primary tumors. Interestingly, the opposite was observed in non-metastatic cancer cells and tumors. Consistent with an active IFN-I response in culture, the metastatic cancer cells displayed elevated levels of cytosolic DNA from both mitochondria and ruptured micronuclei with concomitant activation of cGAS-STING signaling. Interestingly, decreased IFN-I-related gene expression in breast cancer biopsies correlated with an unfavourable prognosis in patients.
Conclusion Our findings show that IFN-I response is dampened in the tumors with the metastatic ability and lower IFN-I expression predicts poor prognosis in triple-negative and HER2 enriched breast cancer patients. This study high- lights the possibility of reactivating the IFN-I response as a potential therapeutic strategy in breast cancer.
Keywords 4T1 model, 66cl4, 67NR, IFN-I, Metastasis
Kristine Pettersen firstname.lastname@example.org Miriam S. Giambelluca email@example.com
Full list of author information is available at the end of the article
The interaction between cancer cells and the tumor microenvironment (TME) profoundly impacts tumor development by influencing processes that lead to either tumor eradication or tumor progression and metasta- sis [1–4]. In a solid tumor, the transformed cancer cells co-evolve with the TME, which includes fibroblasts, endothelial cells and infiltrating immune cells, blood ves- sels, signaling molecules, secreted factors, and extracel- lular matrix . Immune cells are essential components of the TME since a proper antitumor immune response will destroy the transformed cancer cells, while a protu- mor immune response may support tumor growth and metastasis. Cancer cells can avoid immune recognition by actively suppressing antitumor immune responses by releasing anti-inflammatory cytokines, recruiting immunosuppressive immune cells, and shaping the TME towards a more permissive state [6–14].
Interferons (IFNs) have a crucial role in the immune response against infections, intracellular pathogens, and cancer cells. These proteins are released by infected or transformed cells and activate the immune response that promotes cytokine production, natural killer cell functions, and antigen presentation [15, 16]. Type I IFNs (IFN-I), the largest class of IFNs, have a pivotal
role in cancer prevention, inducing anti-tumor immu- nity . Downregulation of IFN-I response prevents CD8+T cell-mediated recognition and elimination of tumor cells. For instance, loss of the type I interferon receptor chain (IFNAR1) in colorectal cancer models led to aggressive cancer growth, while the activation of IFN-I response increases the CD8+T cell effector func- tion and tumor regression [10, 17, 18]. In breast cancer models, downregulation of interferon regulatory factor (Irf7) target genes was associated with increased bone metastasis and reduced survival in this model. On the other hand, high expression of Irf7 regulatory genes correlated with increased metastasis-free survival in more than 800 patients studied .
IFN-I expression can be induced by activating the cGAS-STING pathway, which induced tumor regres- sion in breast, colon cancer and melanoma mouse models when STING agonists were administered [20–
24]. Moreover, STING agonists are currently used in clinical trials in combination with chemotherapy or Programmed Cell Death Ligand 1 (PDL1) antibodies highlighting the importance of IFN in cancer treatment . However, a better understanding of the mecha- nism that controls IFN-I responses and its relationship
in the TME components is needed to extend the suc- cess of this combined therapy.
Despite our improved understanding of anticancer immune responses in the TME, it is still unclear how immuno-suppressive TME are formed and how some cancer cells survive and metastasize . We aimed to identify specific adaptations of metastatic cancer cells that enable them to grow in the TME, avoiding immune response and facilitating tumor progression and metasta- sis. We used cell lines derived from the well-established metastatic mammary carcinoma model 4T1 with differ- ent metastatic capacities. We used 66cl4 cells that metas- tasize to the lungs after injection into the mammary fat pad of mice, while 67NR cells do not metastasize .
Using the unbiased approach of transcriptomics and proteomics to compare metastatic and non-metastatic cancer cells grown in culture versus their correspond- ing tumors, we showed that the IFN-I response differed.
Specifically, we found a significant dampening in the IFN-I response in the metastatic tumors compared to the cells in the culture. In contrast, an increase in the IFN response was observed in the non-metastatic tumor. In addition, we found that elevated IFN-I response in meta- static cancer cells was related to the high cytosolic DNA levels and activation of their sensor system. Our results suggest that factors in the TME enable metastatic tumors to silence their IFN-I response, thus avoiding the antitu- mor immune response. Hence, a better understanding of the mechanism used by metastatic tumors to dampen the local IFN-I signaling could lead to novel targeted therapies to reactivate local immune reactions and boost responses to conventional therapies.
Cell lines and cell culture
67NR and 66cl4 cells, obtained from Barbara Ann Karma- nos Cancer Institute, and MDAMB453 and MDAMB231 cells, kindly provided by Dr. Kaisa Lehti were cultivated as described in Additional file 1: Methods.
RNA from 66cl4 and 67NR cells, as well as from 66cl4 and 67NRprimary mammary tumors from BALB/cJ mice were isolated and sequenced as described before  and analyzed as in Additional file 1: Methods.
Eight- to twelve-week-old female BALB/cJ mice were obtained from Janvier Labs, France. The tumors were ini- tiated and resected and processed as in Additional file 1:
Mass spectrometry analysis
Proteins were isolated from 66cl4 and 67NR mammary breast tumors from mice by homogenization in lysis buffer and analyzed by LC–MS/MS as described in Addi- tional file 1: Methods. The proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  partner repository with the dataset identifier PXD037288.
Quantitative real‑time PCR
Total RNA from cells in culture was extracted using RNe- asy Mini Kit (Qiagen). RNA concentration and purity were measured by Nanodrop. cDNA synthesis followed by qPCR was performed as described in Additional file 1:
Cells were harvested in 8 M urea lysis buffer (8 M urea, 0.5% (v/v) Triton X-100, 100 mM DTT, 1 × Complete® protease inhibitor (Roche) and 2 × phosphatase inhibi- tor cocktail II and III (Sigma)). When indicated, the cells were pretreated with the cGAS inhibitor (Invivogen, # inh-ru521) or the TBKI inhibitors MRT67307 and BX795 (Sigma, #HY-13018 and # HY-10514). Frozen tumor tis- sues were thawed in urea lysis buffer and homogenized as described under sample preparation and MS analyses in Supplementary Methods. Protein concentration was measured and subjected to western blot as in Supple- mentary Methods.
67NR and 66cl4 cells were cultured in full growth medium for three days until they reached 80–90% con- fluency. Conditioned medium (CM) was collected, cen- trifuged, and filtered through a 0.22 µm filter. CXCL10 levels were determined using Mouse CXCL10/IP-10/
CRG-2 DuoSet ELISA (R&D systems, #DY466) according to the manufacturer’s protocols. The data was analyzed using Microplate Manager 6 (Bio-Rad).
Cells were grown on high precision cover glass until desired confluency, fixed, permeabilized, stained with antibodies specific for cGAS and Lamin A and imaged as specified in Additional file 1: Methods.
Analysis of mitochondrial membrane potential and reactive oxygen species.
Mitochondrial membrane potential (MMP) and pro- duction of reactive oxygen species (ROS) were assessed as described in Additional file 1: Methods.
Detection of total and cytosolic mtDNA
Total and cytosolic DNA were isolated from 66cl4 and 67NR cells and subjected to qPCR as described in Addi- tional file 1: Methods.
Use of public databases
Kaplan–Meier plotter , Broad Institute Cancer Cell Line Encyclopedia (CCLE), (https:// porta ls. broad insti tute. org/ ccle)  and cBioPortal  were used as described in Additional file 1: Methods.
Statistical analyses were performed in GraphPad Prism 9. Values are expressed as mean ± standard deviation (SD) or standard error of the mean (SEM) if not other- wise stated. Details about statistical analyses are speci- fied in the figure legends. p-value < 0.05 was considered statistically significant and is labeled with *, p < 0.01 is labeled with **, p < 0.001 is labeled with *** and p < 0.0001 is labeled with ****.
IFN‑I‑associated gene expression is suppressed in metastatic tumors
To identify transcriptome dynamics that occur during metastatic tumor development, we compared the RNA- sequencing profile of cell cultures and tumors formed by the metastatic 66cl4 cells. This analysis identified 1859 genes that were differentially expressed between 66cl4 cells grown in culture versus their corresponding pri- mary tumors (log2 Fold Change (FC) > ± 1.5; adjusted p-value < 0.05). Of these, 1537 genes were significantly higher expressed, whilst 322 genes were significantly lower expressed in the 66cl4 tumors (Fig. 1A). To under- stand the biological processes (BP) linked to the differ- entially expressed genes, we performed gene ontology (GO) enrichment analysis. This showed that the highly expressed genes in the 66cl4 tumors were associated with inflammation and cell chemotaxis (Additional file 1: Fig.
S1A) and that the lower expressed genes were associated with viral defense and IFN-I response (Fig. 1B).
To investigate if we could extend these observations to a non-metastatic tumor, we compared the transcrip- tomes of the non-metastatic 67NR cell line grown in culture versus its primary tumor. This analysis identi- fied 1084 genes differentially expressed between the 67NR samples (log2FC > 1.5; adjusted p-value < 0.05). Of these, 938 genes were significantly elevated, whilst 146 were reduced in the tumors (Additional file 1: FigS1B).
GO analysis for BP revealed that the high-expressed genes were also involved in inflammation and leukocyte
migration (Additional file 1: Fig.S1C), while the low- expressed genes were related to RNA metabolism (Addi- tional file 1: Fig.S1D). Together, the results obtained from the 66cl4 and 67NR analyses showed that genes with a lower expression are involved with different BP in the metastatic and non-metastatic tumors, raising the pos- sibility that these signaling pathways are associated with the different metastatic ability of the tumors.
To further understand the dynamic changes in gene expression between cell lines and tumors, we compared the differentially expressed genes in 66cl4 and 67NR, both when grown in culture and when forming primary tumors (Additional file 1: Fig.S2). Compared to the 67NR cells, 411 genes were highly expressed in 66cl4 cells (log2FC > 1.5). Strikingly, 11 genes stood out as being significantly elevated in 66cl4 cells in culture but also among the significantly downregulated genes in the 66cl4 tumors. GO analysis for BP of these 11 genes revealed that they were involved in IFN response, especially in IFN-I signaling (Fig. 1C). Interestingly, these 11 tran- scripts were amongst those showing low expression in 67NR cells in culture but higher expression in the 67NR tumors (Fig. 1D).
To confirm that the expression of these 16 genes was reduced in metastatic cancer cells following tumor for- mation, we performed RNA sequencing from isolated non-immune cell-enriched (CD45-negative) and immune cell-enriched (CD45-positive) from 66cl4 and 67NR tumors. This analysis showed a significantly lower expres- sion of these 11 genes in 66cl4 isolated from CD45-neg- ative population enriched with cancer cells compared to (CD45-negative) population from 67NR (Fig. 1E, Addi- tional file 1: Fig.S3A). The reduction in expression of 11 genes was also observed in the CD45-positive popula- tion from 66cl4 tumor (Additional file 1: Fig S3B). This suggests that metastatic and non-metastatic cells utilize different strategies to successfully form a tumor, and that alterations in IFN-I signaling stand out as strikingly different.
Type I IFN‑associated proteins are lower in the metastatic tumor
To investigate whether the transcriptional differences associated with the IFN-I response in metastatic and non-metastatic tumors correlate with protein levels, we analyzed the proteomes of the 66cl4 (n = 6) and 67NR (n = 5) primary tumors. Principal component analy- sis of the 5906 detected proteins showed a high degree of similarity between the biological replicates of each tumor (Additional file 1: Fig. S4A). In addition, the tumor samples were separated by tumor type (67NR vs 66cl4) based on the relative abundance of individual
Fig. 1 IFN-I-related gene expression is suppressed in metastatic cancer cells. A Volcano plots depicting differentially expressed genes from 66cl4 primary tumors vs 66cl4 cells. Red points represent genes with Log2foldchange within the cut-off (± 1.5) and adjusted p-value < 0.05. B Gene Ontology (GO) analysis of the most enriched biological processes (BP) associated with the genes with a reduced expression in 66cl4 tumor versus 66cl4 cell line. C Gene Ontology analysis of the most enriched biological processes of the 11 genes highly expressed in 66cl4 versus 67NR cells but low expressed in 66cl4 versus 67NR tumors. D Heatmap showing transcript per million (after log10 transformation) of the 11 genes that are oppositely expressed in cells in culture and in primary tumors of 67NR and 66cl4. E Significantly upregulated IFN-I genes in 66cl4 cells vs. 67NR cells (N = 3) and downregulated genes in 66cl4 (CD45-) vs 67NR (CD45-) population (N = 5) sorted from the primary tumors of 66cl4 and 67NR
proteins, indicating differential protein levels between them (Additional file 1: Fig.S4A). Compared with 67NR tumors, 66cl4 tumors displayed elevated levels of 387 proteins (log2FC > 1.5) and lower levels of 328 proteins (log2FC < − 1.5) (Additional file 1: Fig. S3B). To identify the main biological processes associated with the identi- fied proteins, we performed GO (BP) enrichment analy- ses of the tumor proteomes. Consistent with the RNA sequencing-based data, primary tumors formed by 66cl4 expressed higher levels of proteins related to leukocyte migration and chemotaxis (Fig. 2A). In contrast, pro- teins related to adaptive immune response and cytotoxic- ity were markedly reduced in 66cl4 tumors versus 67NR tumors (Fig. 2B). We also noticed significantly lower lev- els of interferon beta-associated proteins in metastatic
66cl4 tumors versus 67NR tumors (Fig. 2B), suggest- ing that reduced transcription of the interferon-related genes in metastatic tumors correlates with a significant decrease in IFN-I associated proteins.
We further examined the proteins associated with the response to interferon beta and other interferon- related proteins selected based on the literature . We found low levels of 14 IFN-related proteins among the less abundant proteins (log2FC < − 1.5) in 66cl4 tumors (Fig. 2C). These were compared with the 11 ‘oppositely’
expressed genes that were identified earlier (high in 66cl4 vs. 67NR cells in culture but low in 66cl4 vs 67NR in tumors), and six proteins were detected by proteom- ics. Amongst these, three proteins corresponded to tran- scripts that were dampened in 66cl4 tumors (Fig. 1D):
Fig. 2 IFN-I proteins are dampened in metastatic tumors. A GO (BP) functional enrichment analyses of proteins with elevated levels in 66cl4 tumors (N = 6) relative to 67NR tumors (N = 5), from MS analysis (log2FC ≥ 1.5, p-value < 0.05). B GO (BP) functional enrichment analyses of proteins with a reduced expression in 66cl4 tumors (N = 6) relative to 67NR tumors. C Significantly downregulated IFN-I proteins in 66cl4 tumors. D CXCL10 levels in conditioned medium from 67NR and 66cl4 cells determined by ELISA. Bars represent means ± SEM (N = 3). E) CXCL10 immunoblot of protein extracts from 67NR and 66cl4 cell lines (N = 4). F) CXCL10 immunoblot of protein extracts from 66cl4 cell line (N = 1) and 66cl4 tumors (N = 6).
Statistical significance was determined using Student’s t-test (*p < 0.05, **p < 0.01, ***P < 0.001 and ****p < 0.0001)
IFIT3, IFIT1 and ISG15. Two proteins, OAS3 and GBP2, had significant lower expression in 66cl4 tumors versus 67NR tumors, however, these were outside the cutoff of log2FC < − 1.5. Only one protein, GBP2B, did not corre- late with its detected mRNA levels. Among the five unde- tected proteins in MS (CXCL10, TRIM30d, IRF7, GBP3 and CCL5), we chose CXCL10, CCL5 and IRF7 for fur- ther validation [34, 35]. This analysis showed that both mRNA and protein levels of CXCL10, CCl5 and IRF7 protein levels were elevated in metastatic cancer cells in culture and reduced in 66cl4-derived primary tumors (Fig. 2D–F and Additional file 1: Fig.S4C–I). Together, the transcriptomic and proteomic analyses of the cancer cells in culture and primary tumors indicate the dampen- ing of the IFN-I response as the most evident adaptation during tumor formation in the metastatic model.
Cytosolic nuclear DNA is elevated in the murine metastatic and aggressive human cancer cells
The data presented above is consistent with a constitutive IFN-I response in metastatic 66cl4 cells in culture that is downregulated in tumors formed by these cells. Thus, the response is dynamically regulated. We next wondered how the IFN-I response can be constitutively activated in sterile cell culture conditions. IFN-I response is induced by cytosolic DNA . Often cancer cells contain cyto- solic micronuclei produced by chromosomal mis-seg- regation events during mitosis . Immunostaining of DNA and confocal microscopy revealed a similar number of micronuclei in the metastatic and non-metastatic cells (Fig. 3A, B). However, the activation of the IFN-I signal- ing depends on cGAS physically binding to DNA, a con- dition that is met at a subset of micronuclei characterized
Fig. 3 Metastatic and invasive cancer cells display elevated levels of cGAS-positive micronuclei. A Representative immunofluorescence staining of micronuclei in 67NR and 66cl4 cells with cGAS (Green) and Lamin A (red) antibodies. Micronuclei and cGAS-positive micronuclei are highlighted by white arrows. DNA was stained with Hoechst (Blue). Scale bar: 5 μm. B–C Percentage of micronuclei per cell (B) and cGAS-positive micronuclei per cell (C) calculated from three independent experiments (N = > 1000 cells per experiment, from three independent experiments). D–F Representative immunofluorescence staining and quantitation of micronuclei and cGAS-positive micronuclei in MDAMB453 and MDAMB231 cells (N = > 1000 cells per experiment, from three independent experiments). Scale bar: 5 μm. Bars represent mean ± SEM (**p < 0.01, ****p < 0.0001;
by a ruptured nuclear envelope [37, 38]. Strikingly, the fraction of cGAS-positive micronuclei was considerably higher in 66cl4 cells than in 67NR (Fig. 3C). Together, transcriptome, proteome and signaling analyses suggest that cGAS binds to DNA in the cytosol of 66cl4 cells in culture and cause a constitutive IFN-I response in these metastatic cancer cells that is not detected in the non- metastatic cells. As 66cl4 and 67NR are mouse-derived breast cancer cell lines, we next postulated whether simi- lar differences in IFN-I expression could be identified in human breast cancer cell lines. We then analyzed the expression of two well-known interferon-induced genes, IFIT3 and IFI44, using mRNA expression data from the Cancer Cell Line Encyclopedia. Interestingly, the expres- sion of these two transcripts showed a strong associa- tion with each other (Additional file 1: Fig.S5A). Still, their expression varied among the different cancer cells, including the 60 breast cancer cell lines in the database (Additional file 1: Fig. S5B). For instance, IFIT3 and IFI44 mRNA were highly expressed in the invasive human breast cancer cell line MDAMB231 [39, 40], while their expression was low in the non-invasive MDAMB453 (Additional file 1: Fig. S5B). These findings were con- firmed by transcript and protein quantification of IFIT3 in cell extracts of MDAMB231 and MDAMB453 cells (Additional file 1: Fig. S5C–E). In line with the obser- vations seen in mouse breast cancer cell lines, a higher number of micronuclei and cGAS-positive micronuclei were observed in the cytosol of the invasive MDAMB231 compared to non-invasive MDAMB453 human can- cer cells (Fig. 3D–F). Together, our data suggest that the IFN-I response could be associated with constitutive acti- vation of the DNA sensor cGAS by recognising nuclear DNA in the cytosol of metastatic and aggressive cancer cells.
Mitochondrial DNA in the cytosol triggers IFN‑I response in metastatic cancer cells
The IFN-I response can be activated by cytosolic DNA from other sources, such as mitochondria. We therefore measured the levels of mitochondrial DNA (mtDNA) from intact cells and the cytosolic fractions of 66cl4 and 67NR cells. The mtDNA was measured via qPCR using different mtDNA primers (COX1, Dloop1 and Dloop2).
Total mtDNA levels were lower in the 66cl4 cells than 67NR (Fig. 4A and Additional file 1: Fig.S6A). However, in the cytosolic fraction, significantly higher levels of mtDNA were detected in 66cl4 cells (Fig. 4B and Addi- tional file 1: Fig.S6B).
Mitochondrial stress can trigger the release of mtDNA into the cytosol. We, therefore, asked if mito- chondria function differently between the metastatic and non-metastatic cells. We performed flow cytometry
analysis of the mitochondria mass (mitotracker green;
MTG), membrane potential (tetramethylrhodamine ethyl ester perchlorate; TMRE) and mitochondrial ROS production (MitoSox) using fluorescent probes.
Despite the mitochondrial mass being similar between the cancer cells, the membrane potential was higher in 66cl4 compared with 67NR cells (Fig. 4C–D). Indeed, the TMRE/MTG ratio, was significantly higher in 66cl4 cells, indicating hyperpolarized mitochondria (Fig. 4E).
To further understand if hyperpolarized mitochondria is associated with mtROS production in 66cl4 cells, we quantified mtROS levels. This showed a higher num- ber of 66cl4 cells were positive for mtROS compared to 67NR cells at the basal state (Fig. 4F–H). Furthermore, when the cells were treated with an electron transport chain inhibitor (rotenone), 67NR cells showed a six- fold increase in mtROS levels, while 66cl4 cells showed less than a two-fold increase (Additional file 1: FigS6C).
These results suggest that mitochondria in the meta- static 66cl4 cells work at maximum capacity.
Altogether, these data indicate that metastatic cells have poor mitochondrial quality leading to mitochon- drial stress and mtDNA release in the cytosol.
The cGAS‑STING pathway regulates IFN‑I response in metastatic cancer cells
Our results show that the IFN-I response is constitu- tively active in the metastatic cancer cells in culture, while the IFN-I response is dampened when these metastatic cancer cells form a tumor. A better under- standing of the mechanisms that activate this response is important to comprehend how this response could be reactivated in tumors as a therapeutic strategy. Cyto- solic DNA is sensed by the cGAS-STING pathway, which is triggered in response to foreign or self-DNA and can activate the IFN response . Transcrip- tome analysis  and qPCR validation showed that the metastatic 66cl4 cells had higher Sting mRNA than 67NR cells (Additional file 1: Fig.S7A). In line with this, STING protein level was also higher in 66cl4 (Addi- tional file 1: Fig.S7B). Upon activation, STING recruits and phosphorylates TBK1 and IRF3 to induce the pro- duction of type I IFNs . Chemical inhibition of cGAS or TBK1 led to significantly reduced production of the IRF3 target CXCL10 in 66cl4 cells (Fig. 5A–D), indicating that the cGAS-STING-TBK1 pathway is important for IFN-I expression in the metastatic mouse breast cancer cells. Also, in invasive human breast can- cer cells MDAMB231, the protein level of STING and phosphorylated TBK1 was higher than in the non- invasive MDAMB453 cells (Additional file 1: Fig.S7C- D). Together, these results indicate that elevated IFN-I
response in both mouse and human breast cancer cell cultures is associated with activation of the cGAS- STING pathway. The markedly dampened IFN-I in tumors formed from these invasive/metastatic cell lines suggest that the signaling in this pathway is disrupted in vivo to allow such cancer cells to form a metastatic tumor.
Lower IFN‑I expression correlates with a poor prognosis in breast cancer patients
To examine if an active IFN-I response has a prognos- tic relevance in breast cancer patients, we performed a
meta-analysis using the Kaplan–Meier plotter database  of gene expression in breast cancer biopsies.
We analysed whether the oppositely expressed tran- scripts encoding IFN-I-related genes correlated with prognosis monitored as relapse-free survival and over- all survival. For this, we used data from aggressive tri- ple negative breast cancer (TNBC) and HER2 enriched breast cancer patients, which were compared with the ER positive patient group [44–47]. TNBC and HER2 enriched subtypes are both characterized by the lack of estrogen and progesterone receptor expression, although only HER2 enriched subtype express human epidermal Fig. 4 Mitochondrial DNA release into the cytosol in metastatic cell lines associates with mitochondrial stress. A Relative amount of total (A) and cytosolic (B) mitochondrial DNA (mtDNA) in 67NR and 66cl4 cells, normalized to 18s. Bars represent mean ± SEM (N = 3, each in triplicate, one sample t-test, ****p < 0. 0001). C–D Representative histograms of MTG (C) and TMRE (D) in 67NR and 66cl4 cells. E TMRE/MTG ratio in 67NR and 66cl4 cells. Bars represented mean ± SEM (N = 3, one sample t-test, *p < 0.05). F–G Representative histograms showing MitoSox positive populations in 67NR (F) and 66cl4 cells (G). Rotenone was used as a positive control. Black box represents the gating strategy to identify positive cells. H Bars represent mean ± SEM of MitoSOX positive cells in 67NR and 66cl4 cells (N = 4, t-test, and ****p < 0. 0001)
Fig. 5 IFN-I expression in invasive cancer cells is dependent on cGAS-TBK1 signaling. A Representative CXCL10 and ERK1/2 immunoblot of protein extracts of 66cl4 cell line treated with and without cGAS inhibitor (RU.521, 6 µM, 24 h). B Quantification of A. Bars represented mean ± SEM relative to ERK1/2 (N = 3, one sample t-test, **p < 0.01). C Representative CXCL10 immunoblot of protein extracts of 66cl4 cell lines treated with and without TBK1 inhibitors (BX795: 1 µM, 5 µM and MRT67307: 1 µM, 5 µM, 6 h). D Quantification of C. Bars represented mean ± SEM relative to ERK1/2 (N = 3, ANOVA, Dunnett’s multiple comparisons test, ****p < 0.0001)
Fig. 6 Low IFN-I expression correlates with poor relapse-free survival in breast cancer patients. A–C Analysis of relationships between gene expression and relapse free survival (RFS) in breast cancer patients using the online tool KM plotter. High and low expression were defined as above and below median. Relationship between mean expression of IFN-I related genes (n = 11) in ER positive (A), TNBC (B) and HER2 enriched subtypes (C). HR, hazard ratio. D Dot plot showing a positive correlation between mRNA expression of IFIT3 and IFI44 in METABRIC cohort (N = 1904). E Dot plot showing a positive correlation between mRNA expression of cGAS and CXCL10 in METABRIC cohort
growth factor 2 (HER2). In addition, TNBC and HER2 enriched are more aggressive subtypes compared to the ER positive [44, 48–51].
This analysis showed that lower expression of IFN-I related genes correlate with a reduced relapse-free sur- vival in TNBC as well as HER2 positive patients but not in ER positive cancer patients (Fig. 6A–C). In addition, when we evaluated the overall survival, we found that lower levels of IFN-I related genes in tumor biopsies were unfavorable markers exclusively for TNBC patients (Additional file 1: Fig.S8A–C). We extended these results using the public dataset from the “Molecular taxonomy of breast cancer international consortium” (METABRIC) cohort , which facilitated analysis of mRNA expres- sion data from 1904 patients. Here, we observed a strong correlation between several IFN-I genes. For instance, IFIT3 correlated with IFI44 (Fig. 6D), CXCL10, STAT1, IFIT1, IFI44L, ISG15, IRF9 and several cytotoxic T cell markers including CD8A, CD8B, CD28 (Additional file 2:
Table S1). In addition, cGAS expression correlated sig- nificantly with the expression of several IFN-I induced genes (Fig. 6E and Additional file 3: Table S2). Together these results suggest that lower levels of IFN-I related genes in more aggressive cancer subtypes are associated with unfavourable prognosis.
Although all cancer cells can form a tumor, metastatic cancer cells must endure unique adaptations that help them to undergo immune escape and spread to distant sites . To identify these unique adaptations, we com- pared metastatic and non-metastatic breast cancer cells when grown in culture, and in the tumors these cells induce when injected in immune-competent mice. We found that the IFN-I response was oppositely regulated in metastatic and non-metastatic cells versus their cor- responding tumors. While the IFN-I response was active in the metastatic cell lines when grown in culture, it was markedly dampened in the primary tumor. Interest- ingly, we also found that the IFN-I response was active in human breast cancer cells in culture, which is asso- ciated with an invasive phenotype. Further, the IFN-I response was not active in the non-metastatic cell lines, yet it was induced in the primary tumors. This adap- tation may be fundamental to avoid the anti-tumor immune response and metastasis. It is well appreciated that dampened IFN-I signaling aids tumor progression . IFN response can be activated in both cancer cells and immune cells inside the tumor; however, the func- tion is different . In cancer cells, the IFN-I response acts as an alarm system, alerting the immune system to kill the transformed cancer cells. In immune cells, IFN-I
response can act as an effector system that contributes to eliminating cancer cells via T cell priming and effec- tor cytokine production [54, 55].The selection of can- cer cells in a growing tumor likely involves acquirement of mechanisms that downregulate anti-tumor immu- nity, including IFN signaling but how cancer cells turn off IFN-I response in the tumors remains incompletely understood. In any case, these findings further under- score potential therapeutic strategies involving reactiva- tion of the IFN-I response to trigger anti-tumor immune reactions. The IFN-I response has long been a key con- tributor to effective antiviral responses. The induction of IFN-I signaling is essential for the immune system to eliminate cells infected with viruses and intracellular bacterial infections. During infection, the presence of foreign DNA in the cytosol leads to activation of cGAS and subsequent induction of the IFN-I response [53, 55, 56]. Here, we identified elevated levels of cytosolic DNA from micronuclei and mitochondria in metastatic cancer cells in sterile culture conditions. In murine metastatic and invasive human breast cancer cell lines, cytoplasmic DNA foci were often associated with cGAS. This suggests that micronuclei are ruptured and sensed by cGAS. Our data is supported by previous reports showing that dis- ruption of the micronuclear envelope exposes self-DNA to the cytosol, followed by recruitment of cGAS and acti- vation of cGAS-STING signaling [38, 57]. Our observa- tion of mtDNA leakage is supported by our previous findings that show dysfunctional mitochondrial in meta- static cancer cells, characterized by high mtROS produc- tion, hyperpolarized mitochondria with higher proton leak, and lower respiratory capacity . We therefore speculate that elevated levels of mitochondrial compo- nents and hyperpolarized mitochondria could be a com- pensatory mechanism to increase ATP production, but also result in mitochondrial damage and mtDNA release in the cytosol. Here we show that the IFN-I response is constitutively active in breast cancer cells with metastatic ability grown in culture, while an effective dampening of this response occurs when they grow as tumors. Likely, this downregulation is either due to stimulated removal of DNA from the cytosol or downregulation of the cGAS- STING signaling pathway in the cancer cells within the tumor. While IFN-I inducers are well-known, negative regulation of IFN-I signaling is poorly understood .
Nevertheless, it is well established that cytosolic DNA is degraded by autophagy [60–63]. Even if dampening of the IFN-I response involves elevated autophagy in the cancer cells of a tumor, it remains unknown which factors of the TME cause this effect. It is tempting to speculate that local nutrient restriction could stimulate autophagy in cancer cells of solid tumors. However, the
ability to accurately quantify autophagic flux in biopsies is currently limited.
In human tumor biopsies, elevated IFN-I signaling correlates with induced T cell responses against tumor specific antigens [64, 65] and it may represent a mecha- nism that limits tumor development. In line with this, our METABRIC analysis of 1904 breast cancer patients showed that the expression of several interferon- induced genes correlates with several T cell markers.
Using KM plotter, we observed that reduced expression of the 11 oppositely expressed IFN-related genes pre- dict poor prognosis in both TNBC and HER2 enriched aggressive breast cancer subtypes but not in ER posi- tive subtypes. In the TME, IFN signaling is commonly induced by tumor-associated antigens or due to immu- nogenic cell death in response to chemotherapy and radiotherapy . In TNBC and HER2-enriched breast cancers patients that undergo trastuzumab monother- apy or in combination with chemotherapy, high tumor- infiltrating lymphocytes were associated with a better prognosis in patients. In contrast, no association was observed in ER-positive patients [67–70]. This could be because CD8+ T cells can produce and respond to IFNs mediating antitumor responses . While systemic IFN-I based therapies have been shown to increase the efficacy of the checkpoint inhibitors in TNBC, no pre- dictive values were obtained in the ER-positive [68, 72] patients. This leads to an open question of whether inducing IFN-based immune therapy is beneficial to ER positive patients, which still needs to be addressed.
Here, our results highlight the clinical significance of an elevated IFN-I response, supporting the therapeutic potential of increasing IFN-I response in patients where these responses are suppressed. One limitation of this type of data analysis, is the lack of evidence about which cells, immune or non-immune, contributes to the IFN-I response in the patient biopsies. Likely, reduction of IFN response could be either due to the downregulation of immune components that induce IFN-I signaling or due to the stimulated removal of the endogenous DAMPs in the cancer cells in the tumor as mentioned earlier in the discussion [73–75].
Currently, more than 370 (recruiting or active) clini- cal trials aiming to target IFN-I signaling in cancer patients are ongoing (www. clini caltr ials. gov). However, IFN-associated toxicity has been a significant obstacle for this strategy to be translated to the clinic. Recently, other approaches to activate IFN-I response have been explored including cGAMP-based nanoparticles.
cGAMP, is a second messenger that is synthesized in response to cytosolic double stranded-DNA. These nano- particles enhance the cytosolic delivery of cGAMP and trigger formation of an immune competant TME with
enriched T cell infiltration . Alternatively, tumors that are non-responsive to immune checkpoint inhibi- tors could be transformed into the immune competent tumors by using STING agonist-mediated T-cell prim- ing and infiltration [12, 77–81]. Since STING agonists can redesign the TME to promote stronger antitumor T cell responses [22, 78], they are good candidates for com- bination with established immunotherapies. However, despite several completed and ongoing Phase II studies detecting signs of clinical activity for STING agonists, no Phase III studies have been registered yet. Even with the best-characterized STING agonist DMXAA, most of the trials with mono and combination therapy have failed due to low efficacy and toxicity issues . These findings highlight the importance of identifying better therapeu- tic combinations and improving understanding of the underlaying mechanisms controlling this signaling in a complex tumor.
In this study, we utilized an immunocompetent mouse model of breast cancer to demonstrate that IFN-I sign- aling represents an important mechanism supporting tumor progression. Further research is needed to uncover the full repertoire of mechanisms that control this immu- nological switch and find novel strategies to efficiently target aggressive tumors, reduce the risk of metastasis and improve the survival of breast cancer patients.
ncRNA Noncoding RNA
Rib Prot Ribonucleoprotein
End Endogenous pathway
cGAS Cyclic GMP-AMP synthase
cGAMP Cyclic guanosine monophosphate–adenosine monophosphate
STING Stimulator of interferon genes CD45 Cluster of differentiation 45
IFIT3 Interferon-induced protein with tetratricopeptide repeats 3
IFIT1 Interferon-induced protein with tetratricopeptide repeats 1
ISG15 Interferon-stimulated gene 15 IRF3 Interferon regulatory factor 3 IRF7 Interferon regulatory factor 7 XAF1 XIAP-associated factor 1 IGTP Interferon gamma induced GTPase
STAT1 Signal transducer and activator of transcription 1 OAS3 2′-5′-Oligoadenylate synthetase 3
GBP2 Guanylate binding protein 2 CXCL10 C-X-C motif chemokine ligand 10 Trim30d Tripartite motif-containing 30D IFF7 Interferon regulatory factor 7 H2-T22 Histocompatibility 2, T region locus 22 GBP3 Guanylate binding protein 3
CCL5 C–C motif chemokine ligand 5
H2-Q4 Histocompatibility 2, Q region locus 4 IFI44 Interferon induced protein 44 COX1 Cytochrome c oxidase subunit 1 COX2 Cytochrome c oxidase subunit II
Dloop1 Displacement loop1
Dloop2 Displacement loop1
TBK1 TANK binding kinase 1
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s12964- 023- 01062-y.
Additional file 1. Supplementary figures S1 to S8 and supplementary methods.
Additional file 2. Correlation between IFN-I induced genes expression.
Additional file 3. Correlation between cGAS and IFN-I induced genes expression.
We would like to thank Ulrike Neckmann and Camilla Wolowczyk for provid- ing access/inputs for the transcriptome data. Professor Kaisa Lehti and Dr.
Andrew Single for reading and providing valuable insights for the manuscript, Proteomics and Modomics Experimental Core Facility (PROMEC), NTNU for proteomics, Membrane dynamics in tumorigenesis group (OUH) for access to imaging facilities.
AL, SBA, MV, IJ, ABA and FR performed experiments and analyzed data. NJK analyzed part of the proteomics data. MSG and GB designed and super- vised the study. AL, SBA, MV, IJ, KP, MSG and GB interpreted data. AL, SBA, KP prepared the first draft. AL, MSG, and GB prepared the final manuscript. MV provided valuable insights for the final manuscript. All authors contributed to the article and approved the submitted version.
Open access funding provided by Norwegian University of Science and Technology. This work was supported by grants from the Norwegian Cancer Society (project number #6846671 and #419654), Research Council of Norway (Centres of Excellence funding program, # 223255/F50), Central Norway Regional Health Authority (project number #90181900), PhD-grant to AL from
NTNU, Faculty of Natural Sciences. Additional funding from Southern and East- ern Norwegian Health Authority (#2018043, MV), Tromsø Research Foundation and Trond Mohn Foundation (180°N project, MSG).
Availability of data and materials
The mass spectrometry proteomics data have been deposited to the Pro- teomeXchange Consortium via the PRIDE  partner repository with the dataset identifier PXD037288. All other data and materials mentioned in this article can be requested by email.
Declarations Competing interest
The authors declare that they have no competing interests.
Ethics approval and consent to participate
The mice studies were approved by the National Animal Research Authorities and carried out according to the European Convention for the Protection of Vertebrates used for Scientific Purposes (FOTS ID 17895 and FOTS: 26021).
All experiments were performed according to approved guidelines and regulations.
Consent for publication Not applicable.
1 Department of Biomedical Laboratory Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, Trondheim, Norway. 2 Centre of Molecular Inflammation Research and Department of Cancer Research and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway. 3 Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Montebello, Oslo, Norway. 4 Department of Molecular Cell Biology, Institute for Cancer Research, Oslo University Hospital, Montebello, Oslo, Norway. 5 Institute for Cancer Research, Department of Tumor Biology, Oslo University Hospital, Montebello, Oslo, Norway. 6 Department of Circula- tion and Medical Imaging, Faculty of Medicine and Health Sciences, Norwe- gian University of Science and Technology, Trondheim, Norway. 7 Department of Clinical Medicine, Faculty of Health Science, UiT-The Arctic University of Nor- way, Tromsø, Norway.
Received: 27 October 2022 Accepted: 30 January 2023
1. Pernot S, Evrard S, Khatib AM. The give-and-take interaction between the tumor microenvironment and immune cells regulating tumor progres- sion and repression. Front Immunol. 2022;13: 850856.
2. Lan HR, Du WL, Liu Y, Mao CS, Jin KT, Yang X. Role of immune regulatory cells in breast cancer: foe or friend? Int Immunopharmacol. 2021;96:
3. Mao X, Xu J, Wang W, Liang C, Hua J, Liu J, et al. Crosstalk between cancer- associated fibroblasts and immune cells in the tumor microenvironment:
new findings and future perspectives. Mol Cancer. 2021;20(1):131.
4. Janssen LME, Ramsay EE, Logsdon CD, Overwijk WW. The immune system in cancer metastasis: friend or foe? J Immunother Cancer. 2017;5(1):79.
5. Jin MZ, Jin WL. The updated landscape of tumor microenvironment and drug repurposing. Signal Transduct Target Ther. 2020;5(1):166.
6. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell.
7. Kartikasari AER, Huertas CS, Mitchell A, Plebanski M. Tumor-induced inflammatory cytokines and the emerging diagnostic devices for cancer detection and prognosis. Front Oncol. 2021;11: 692142.
8. Lan T, Chen L, Wei X. Inflammatory cytokines in cancer: compre- hensive understanding and clinical progress in gene therapy. Cells.
9. Liu YT, Sun ZJ. Turning cold tumors into hot tumors by improving T-cell infiltration. Theranostics. 2021;11(11):5365–86.
10. Katlinski KV, Gui J, Katlinskaya YV, Ortiz A, Chakraborty R, Bhattacharya S, et al. Inactivation of interferon receptor promotes the establish- ment of immune privileged tumor microenvironment. Cancer Cell.
11. Hegde PS, Karanikas V, Evers S. The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of check- point inhibition. Clin Cancer Res. 2016;22(8):1865–74.
12. Zhang J, Huang D, Saw PE, Song E. Turning cold tumors hot: from molecular mechanisms to clinical applications. Trends Immunol.
13. Bonaventura P, Shekarian T, Alcazer V, Valladeau-Guilemond J, Valsesia- Wittmann S, Amigorena S, et al. Cold tumors: a therapeutic challenge for immunotherapy. Front Immunol. 2019;10:168.
14. Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumors with combination immunotherapies. Nat Rev Drug Discov.
15. Yu R, Zhu B, Chen D. Type I interferon-mediated tumor immunity and its role in immunotherapy. Cell Mol Life Sci. 2022;79(3):191.
16. Fenton SE, Saleiro D, Platanias LC. Type I and II interferons in the anti- tumor immune response. Cancers. 2021;13(5):1037.
17. Boukhaled GM, Harding S, Brooks DG. Opposing roles of type I interferons in cancer immunity. Annu Rev Pathol. 2021;16:167–98.
18. Lu C, Klement JD, Ibrahim ML, Xiao W, Redd PS, Nayak-Kapoor A, et al.
Type I interferon suppresses tumor growth through activating the STAT3- granzyme B pathway in tumor-infiltrating cytotoxic T lymphocytes. J Immunother Cancer. 2019;7(1):157.
19. Bidwell BN, Slaney CY, Withana NP, Forster S, Cao Y, Loi S, et al. Silencing of Irf7 pathways in breast cancer cells promotes bone metastasis through immune escape. Nat Med. 2012;18(8):1224–31.
20. Amouzegar A, Chelvanambi M, Filderman JN, Storkus WJ, Luke JJ. STING agonists as cancer therapeutics. Cancers. 2021;13(11):2695.
21. Ohkuri T, Ghosh A, Kosaka A, Zhu J, Ikeura M, David M, et al. STING contributes to antiglioma immunity via triggering type I IFN signals in the tumor microenvironment. Cancer Immunol Res. 2014;2(12):1199–208.
22. Corrales L, Glickman LH, McWhirter SM, Kanne DB, Sivick KE, Katibah GE, et al. Direct activation of STING in the tumor microenvironment leads to potent and systemic tumor regression and immunity. Cell Rep.
23. Chandra D, Quispe-Tintaya W, Jahangir A, Asafu-Adjei D, Ramos I, Sintim HO, et al. STING ligand c-di-GMP improves cancer vaccination against metastatic breast cancer. Cancer Immunol Res. 2014;2(9):901–10.
24. Weiss JM, Guerin MV, Regnier F, Renault G, Galy-Fauroux I, Vimeux L, et al.
The STING agonist DMXAA triggers a cooperation between T lympho- cytes and myeloid cells that leads to tumor regression. Oncoimmunol- ogy. 2017;6(10): e1346765.
25. Yin M, Hu J, Yuan Z, Luo G, Yao J, Wang R, et al. STING agonist enhances the efficacy of programmed death-ligand 1 monoclonal antibody in breast cancer immunotherapy by activating the interferon-beta signal- ling pathway. Cell Cycle. 2022;21(8):767–79.
26. Whiteside TL. The tumor microenvironment and its role in promoting tumor growth. Oncogene. 2008;27(45):5904–12.
27. Miller FR, Miller BE, Heppner GH. Characterization of metastatic heteroge- neity among subpopulations of a single mouse mammary tumor: hetero- geneity in phenotypic stability. Invasion Metastasis. 1983;3(1):22–31.
28. Neckmann U, Wolowczyk C, Hall M, Almaas E, Ren J, Zhao S, et al. GREM1 is associated with metastasis and predicts poor prognosis in ER-negative breast cancer patients. Cell Commun Signal. 2019;17(1):140.
29. Perez-Riverol Y, Bai J, Bandla C, Garcia-Seisdedos D, Hewapathirana S, Kamatchinathan S, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res.
30. kmplot.com (2019) [Available from: https:// kmplot. com/ analy sis/ index.
php?p= servi ce& cancer= breast.
31. Ghandi M, Huang FW, Jane-Valbuena J, Kryukov GV, Lo CC, McDonald ER 3rd, et al. Next-generation characterization of the cancer cell line encyclopedia. Nature. 2019;569(7757):503–8.
32. cBioPortal. cBioPortal.org (2019) [Available from: http:// www. cbiop ortal.
33. Owen KL, Gearing LJ, Zanker DJ, Brockwell NK, Khoo WH, Roden DL, et al.
Prostate cancer cell-intrinsic interferon signaling regulates dormancy and metastatic outgrowth in bone. EMBO Rep. 2020;21(6): e50162.
34. Padovan E, Spagnoli GC, Ferrantini M, Heberer M. IFN-alpha2a induces IP-10/CXCL10 and MIG/CXCL9 production in monocyte-derived dendritic cells and enhances their capacity to attract and stimulate CD8+ effector T cells. J Leukoc Biol. 2002;71(4):669–76.
35. Metzemaekers M, Vanheule V, Janssens R, Struyf S, Proost P. Overview of the mechanisms that may contribute to the non-redundant activities of interferon-inducible CXC chemokine receptor 3 ligands. Front Immunol.
36. Harapas CR, Idiiatullina E, Al-Azab M, Hrovat-Schaale K, Reygaerts T, Steiner A, et al. Organellar homeostasis and innate immune sensing. Nat Rev Immunol. 2022;22(9):535–49.
37. Harding SM, Benci JL, Irianto J, Discher DE, Minn AJ, Greenberg RA.
Mitotic progression following DNA damage enables pattern recognition within micronuclei. Nature. 2017;548(7668):466–70.
38. Mackenzie KJ, Carroll P, Martin CA, Murina O, Fluteau A, Simpson DJ, et al. cGAS surveillance of micronuclei links genome instability to innate immunity. Nature. 2017;548(7668):461–5.
39. von Nandelstadh P, Gucciardo E, Lohi J, Li R, Sugiyama N, Carpen O, et al.
Actin-associated protein palladin promotes tumor cell invasion by link- ing extracellular matrix degradation to cell cytoskeleton. Mol Biol Cell.
40. Sugiyama N, Gucciardo E, Tatti O, Varjosalo M, Hyytiainen M, Gstaiger M, et al. EphA2 cleavage by MT1-MMP triggers single cancer cell invasion via homotypic cell repulsion. J Cell Biol. 2013;201(3):467–84.
41. Jiang M, Chen P, Wang L, Li W, Chen B, Liu Y, et al. cGAS-STING, an impor- tant pathway in cancer immunotherapy. J Hematol Oncol. 2020;13(1):81.
42. Zhang C, Shang G, Gui X, Zhang X, Bai XC, Chen ZJ. Structural basis of STING binding with and phosphorylation by TBK1. Nature.
43. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346–52.
44. Won KA, Spruck C. Triple-negative breast cancer therapy: current and future perspectives (Review). Int J Oncol. 2020;57(6):1245–61.
45. Wang J, Xu B. Targeted therapeutic options and future perspectives for HER2-positive breast cancer. Signal Transduct Target Ther. 2019;4:34.
46. Haffty BG, Yang Q, Reiss M, Kearney T, Higgins SA, Weidhaas J, et al.
Locoregional relapse and distant metastasis in conservatively managed triple negative early-stage breast cancer. J Clin Oncol. 2006;24(36):5652–7.
47. Deepak KGK, Vempati R, Nagaraju GP, Dasari VR, Nagini S, Rao DN, et al. Tumor microenvironment: challenges and opportunities in targeting metastasis of triple negative breast cancer. Pharmacol Res.
48. Yarden Y. Biology of HER2 and its importance in breast cancer. Oncology.
49. Vaught DB, Stanford JC, Young C, Hicks DJ, Wheeler F, Rinehart C, et al.
HER3 is required for HER2-induced preneoplastic changes to the breast epithelium and tumor formation. Cancer Res. 2012;72(10):2672–82.
50. Turke AB, Song Y, Costa C, Cook R, Arteaga CL, Asara JM, et al. MEK inhibi- tion leads to PI3K/AKT activation by relieving a negative feedback on ERBB receptors. Cancer Res. 2012;72(13):3228–37.
51. Li X, Yang C, Wan H, Zhang G, Feng J, Zhang L, et al. Discovery and devel- opment of pyrotinib: a novel irreversible EGFR/HER2 dual tyrosine kinase inhibitor with favorable safety profiles for the treatment of breast cancer.
Eur J Pharm Sci. 2017;110:51–61.
52. Labani-Motlagh A, Ashja-Mahdavi M, Loskog A. The tumor microenviron- ment: a milieu hindering and obstructing antitumor immune responses.
Front Immunol. 2020;11:940.
53. Kawasaki T, Kawai T. Discrimination Between self and non-self-nucleic acids by the innate immune system. Int Rev Cell Mol Biol. 2019;344:1–30.
54. Musella M, Galassi C, Manduca N, Sistigu A. The yin and yang of type I IFNs in cancer promotion and immune activation. Biology. 2021;10(9):856.
55. Hopfner KP, Hornung V. Molecular mechanisms and cellular functions of cGAS-STING signalling. Nat Rev Mol Cell Biol. 2020;21(9):501–21.
56. Zhao J, Xiao R, Zeng R, He E, Zhang A. Small molecules targeting cGAS- STING pathway for autoimmune disease. Eur J Med Chem. 2022;238:
57. Zhang CZ, Spektor A, Cornils H, Francis JM, Jackson EK, Liu S, et al. Chromothripsis from DNA damage in micronuclei. Nature.
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58. Wolowczyk C, Neckmann U, Aure MR, Hall M, Johannessen B, Zhao S, et al. NRF2 drives an oxidative stress response predictive of breast cancer.
Free Radic Biol Med. 2022;184:170–84.
59. Prabakaran T, Bodda C, Krapp C, Zhang BC, Christensen MH, Sun C, et al. Attenuation of cGAS-STING signaling is mediated by a p62/
SQSTM1-dependent autophagy pathway activated by TBK1. EMBO J.
60. Rello-Varona S, Lissa D, Shen S, Niso-Santano M, Senovilla L, Marino G, et al. Autophagic removal of micronuclei. Cell Cycle. 2012;11(1):170–6.
61. Yao M, Wu Y, Cao Y, Liu H, Ma N, Chai Y, et al. Autophagy-mediated clear- ance of free genomic DNA in the cytoplasm protects the growth and survival of cancer cells. Front Oncol. 2021;11: 667920.
62. Maluquer de Motes C. Autophagy takes the STING out of DNA sensing.
Cell Mol Immunol. 2022;19(1):125–6.
63. Han X, Chen H, Gong H, Tang X, Huang N, Xu W, et al. Autolysosomal degradation of cytosolic chromatin fragments antagonizes oxidative stress-induced senescence. J Biol Chem. 2020;295(14):4451–63.
64. Fuertes MB, Kacha AK, Kline J, Woo SR, Kranz DM, Murphy KM, et al.
Host type I IFN signals are required for antitumor CD8+ T cell responses through CD8alpha+ dendritic cells. J Exp Med. 2011;208(10):2005–16.
65. Bertucci F, Ueno NT, Finetti P, Vermeulen P, Lucci A, Robertson FM, et al.
Gene expression profiles of inflammatory breast cancer: correlation with response to neoadjuvant chemotherapy and metastasis-free survival.
Ann Oncol. 2014;25(2):358–65.
66. Krysko DV, Garg AD, Kaczmarek A, Krysko O, Agostinis P, Vandenabeele P.
Immunogenic cell death and DAMPs in cancer therapy. Nat Rev Cancer.
67. Salgado R, Denkert C, Demaria S, Sirtaine N, Klauschen F, Pruneri G, et al.
The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer:
recommendations by an International TILs Working Group 2014. Ann Oncol. 2015;26(2):259–71.
68. Loi S, Michiels S, Salgado R, Sirtaine N, Jose V, Fumagalli D, et al. Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: results from the FinHER trial. Ann Oncol. 2014;25(8):1544–50.
69. Fu X, De Angelis C, Schiff R. Interferon signaling in estrogen receptor-positive breast cancer: a revitalized topic. Endocrinology.
70. Dieci MV, Radosevic-Robin N, Fineberg S, van den Eynden G, Ternes N, Penault-Llorca F, et al. Update on tumor-infiltrating lymphocytes (TILs) in breast cancer, including recommendations to assess TILs in residual disease after neoadjuvant therapy and in carcinoma in situ: a report of the International immuno-oncology biomarker working group on breast cancer. Semin Cancer Biol. 2018;52(Pt 2):16–25.
71. Slaney CY, Kershaw MH, Darcy PK. Trafficking of T cells into tumors. Can- cer Res. 2014;74(24):7168–74.
72. Brockwell NK, Owen KL, Zanker D, Spurling A, Rautela J, Duivenvoorden HM, et al. Neoadjuvant interferons: critical for effective PD-1-based immunotherapy in TNBC. Cancer Immunol Res. 2017;5(10):871–84.
73. Rock KL, Lai JJ, Kono H. Innate and adaptive immune responses to cell death. Immunol Rev. 2011;243(1):191–205.
74. Green DR, Ferguson T, Zitvogel L, Kroemer G. Immunogenic and tolero- genic cell death. Nat Rev Immunol. 2009;9(5):353–63.
75. Arimoto KI, Miyauchi S, Stoner SA, Fan JB, Zhang DE. Negative regulation of type I IFN signaling. J Leukoc Biol. 2018. https:// doi. org/ 10. 1002/ JLB.
2MIR0 817- 342R.
76. Shae D, Becker KW, Christov P, Yun DS, Lytton-Jean AKR, Sevimli S, et al.
Endosomolytic polymersomes increase the activity of cyclic dinucleotide STING agonists to enhance cancer immunotherapy. Nat Nanotechnol.
77. Le Naour J, Zitvogel L, Galluzzi L, Vacchelli E, Kroemer G. Trial watch: STING agonists in cancer therapy. Oncoimmunology. 2020;9(1):1777624.
78. Flood BA, Higgs EF, Li S, Luke JJ, Gajewski TF. STING pathway agonism as a cancer therapeutic. Immunol Rev. 2019;290(1):24–38.
79. Sceneay J, Goreczny GJ, Wilson K, Morrow S, DeCristo MJ, Ubellacker JM, et al. Interferon signaling is diminished with age and is associated with immune checkpoint blockade efficacy in triple-negative breast cancer.
Cancer Discov. 2019;9(9):1208–27.
80. Zheng J, Mo J, Zhu T, Zhuo W, Yi Y, Hu S, et al. Comprehensive elaboration of the cGAS-STING signaling axis in cancer development and immuno- therapy. Mol Cancer. 2020;19(1):133.
81. Sistigu A, Yamazaki T, Vacchelli E, Chaba K, Enot DP, Adam J, et al. Cancer cell-autonomous contribution of type I interferon signaling to the effi- cacy of chemotherapy. Nat Med. 2014;20(11):1301–9.
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